Robot system for performing learning control based on machining results and control method therefor

ABSTRACT

A learning control unit includes a machining device performance calculation section for calculating performance of a machining device during machining or after machining based on a motion command issued to a robot by a controller, a machining command issued to the machining device by the controller, and machining results measured by a sensor, an operation speed correction information calculation section for calculating operation speed correction information of the robot based on the performance of the machining device so as to satisfy a preset acceptable condition of the machining error, and under an allowable load condition of the robot, and a learning completion determination section for determining whether or not learning has completed based on previous correction information and current correction information.

RELATED APPLICATIONS

The present application claims priority of Japanese Application Number2018-024188, filed on Feb. 14, 2018, the disclosure of which is herebyincorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION 1. Technical Field

The present invention relates to a robot control technology, and inparticular, relates to a robot system which performs learning controlbased on machining results and a control method therefor.

2. Description of Prior Art

In applications such as sealing, welding, and laser machining, theprevention of robot vibration leads to improved machining quality.However, in addition to robot vibration, machining quality changes dueto vibration of the workpiece during machining and the performance ofthe machining device performing the sealing, welding, or lasermachining.

For example, when a workpiece vibrates in a system performing lasermachining while the posture of the workpiece changes, there is a limitto the improvement in machining quality which can be brought about bysimply eliminating vibration at the robot tip. In a sealing system,since there is a response delay in the machining device from the time atwhich the sealant flow rate command is issued until the actual machiningis performed, sufficient machining quality cannot be obtained.Furthermore, though there are systems for maintaining a constant beadwidth and bead thickness by changing the sealant flow rate in accordancewith the operation speed of the robot, in such systems, if the operationspeed of the robot changes rapidly, a change in the sealant flow ratecannot be performed quickly enough, and the quality of the sealing maybe reduced.

As technologies related to the present application, the publicationsdescribed below have been proposed. Japanese Unexamined PatentPublication (Kokai) No. 05-104436 discloses a polishing machine havingan integrated tactile sensor, comprising a polisher provided on a robotend effector, a tactile sensor provided integrally with the polisher fordetecting the surface condition of a workpiece, and a machining controlunit for changing the machining conditions of the polishing machine inaccordance with the detected surface condition.

Japanese Unexamined Patent Publication (Kokai) No. 2012-236267 disclosesa machining conditions searching device, comprising a processing machinefor experimentally machining a workpiece in accordance with experimentalmachining conditions, a machining results collection means foraccumulating a combination of machining results of the experimentalmachining and the experimental machining conditions as experimentalmachining data, a first machining characteristic model generation meansfor generating a new machining characteristic model representing therelationship between the machining conditions and the machining resultsusing the experimental machining data, and a second machiningcharacteristics model generation means for generating a new machiningcharacteristics model reflecting a machining pass/fail evaluation whilechanging the pass/fail evaluation in the machining results in theexperimental machining data one-by-one.

Re-publication of PCT Publication No. 2015/098126 discloses a machiningsupport system, comprising a machining unit for machining a workpiece bydriving a machine tool, a workpiece support force generation unit forgenerating a workpiece support force against a machining reaction force,a support device for moving while supporting the workpiece support forcegeneration unit, and a workpiece support force controller forcontrolling the operation of the workpiece support force generation unitand the support device based on machining reaction force related dataand machining position related data.

Japanese Patent No. 6088190 discloses a machining system, comprising amachining device for machining a workpiece, a machining controller formachining a workpiece on the machining device in accordance with amachining program, a workpiece measurement device for measuring theshape of the workpiece, and a measurement controller for controlling theoperation of the robot for measurement and measuring the shape of theworkpiece in the workpiece measurement device, wherein the machiningcontroller is configured so as to correct the machining program based onworkpiece shape information.

SUMMARY OF THE INVENTION

In order to improve machining quality, it is necessary to operate robotsand machining devices in consideration of not only vibration of the tipof the robot but rather the entire system, such as vibration of theworkpiece during machining and performance of the machining device. Theteaching of such operation requires trial and error, and skill of theteacher who teaches and modifies the teaching while actually viewing themachining result, and takes time and effort.

A technology of performing learning control based on machining resultsin consideration of the entire system has been demanded.

An aspect of the present disclosure provides a robot system, comprisinga robot, a machining device for machining a workpiece, a controller forcontrolling the robot and the machining device, a sensor for detectingmachining results, and a learning control unit for performing learningcontrol based on the machining results, wherein the learning controlunit comprises a machining device performance calculation section forcalculating performance of the machining device during machining orafter machining based on a motion command issued to the robot by thecontroller, a machining command issued to the machining device by thecontroller, and machining results measured by the sensor, an operationspeed correction information calculation section for calculatingoperation speed correction information of the robot based on theperformance of the machining device so as to satisfy a preset acceptablecondition of the machining error, and under an allowable load conditionof the robot, and a learning completion determination section fordetermining whether or not learning has completed based on previouscorrection information and current correction information.

Another aspect of the present invention provides a method for thecontrol of a robot system comprising a robot, a machining device formachining a workpiece, a controller for controlling the robot and themachining device, a sensor for measuring machining results, and alearning control section for performing learning control based on themachining results, the method causing the learning control section toexecute the steps of calculating performance of the machining deviceduring machining or after machining based on a motion command issued tothe robot by the controller, a machining command issued to the machiningdevice by the controller, and machining results measured by the sensor,calculating operation speed correction information of the robot based onthe performance of the machining device so as to satisfy a presetacceptable condition of the machining error, and under an allowable loadcondition of the robot, and determining whether or not learning hascompleted based on prior correction information and current correctioninformation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a robot system according to an embodiment.

FIG. 2 is a configuration view of a robot according to the embodiment.

FIG. 3 is a block diagram of the robot system according to theembodiment.

FIG. 4 is a flowchart illustrating a control method of the robot systemaccording to the embodiment.

FIG. 5A is a graph showing a motion command issued to the robot, themachining command issued to a machining device, and an actual beadamount prior to learning.

FIG. 5B is a graph showing a motion command issued to the robot, themachining command issued to a machining device, and an actual beadamount after learning.

FIG. 6A is a plan view showing an actual bead at a corner prior tolearning.

FIG. 6B is a plan view showing an actual bead at a corner afterlearning.

FIG. 7 is a block diagram of a robot system according to anotherembodiment, comprising a plurality of sets of machining systems and aserver device communicable with the plurality of sets of machiningsystems.

DETAILED DESCRIPTION

The embodiments of the present disclosure will be described below withreference to the attached drawings.

In the drawings, the same or equivalent constituent elements areassigned the same or equivalent reference numerals. Furthermore, theembodiments described below do not limit the technical scope of theinventions described in the claims or the meanings of the termsdescribed therein.

FIG. 1 is a schematic view of a robot system 10 according to the presentembodiment and FIG. 2 is a configuration view of a robot 11 according tothe present embodiment. As shown in FIG. 1, the robot system 10comprises a robot 11, a machining device 13 for performing machining ona workpiece 12, a controller 14 for controlling the robot 11 and themachining device 13, a sensor 15 for measuring machining results, and alearning control unit (refer to FIG. 3) for performing learning controlbased on the machining results. Note that though the workpiece 12 issupported by a jig 16 for positioning the workpiece 12, in differentembodiments, the jig 16 may be a positioner for rotating and positioningthe workpiece 12, may be a stage capable of adjusting the position ofthe workpiece 12 in the X-axis, Y-axis, and Z-axis directions, or may beanother robot hand for grasping the workpiece 12.

As shown in FIG. 2, the robot 11 is a known robot manipulator comprisingservomotors, and six joint axes J1 to J6 including speed reducers or thelike. A world coordinate system C1 defined in space and a mechanicalinterface coordinate system C2 defined at the flange position of thejoint axis J6 on the wrist side are defined. The robot 11 comprises anend effector 17 attached to the wrist side of the joint axis J6.

Referring again to FIG. 1, the machining device 13 comprises a sealingdevice for performing sealing of the workpiece 12 in accordance with asealant flow rate command. The sealing device comprises a sealing gun 18attached to the end effector 17 of the robot 11. The sealing gun 18discharges sealant to the workpiece 12 in accordance with the flow ratecommand. In different embodiments, the machining device 13 may comprisean arc welding device for performing arc welding on the workpiece 12 inaccordance with a welding wire feed command, may comprise a lasermachining device for performing laser machining on the workpiece 12 inaccordance with a laser light output command, or may comprise acombination of these.

The controller 14 is wired or wirelessly communicatively connected tothe robot 11, the machining device 13, and the sensor 15. The controller14 issues a motion command to the robot 11, issues a machining commandto the machining device 13, and acquires machining results from thesensor 15. The motion command issued to the robot 11 may include aposition command, a speed command, or the like. The machining commandissued to the machining device 13 may include a flow rate command, afeed command, an output command, or the like in accordance with theconfiguration of the machining device 13. The controller 14 enablesuniform machining of the workpiece 12 by changing the machining commandissued to the machining device 13 in accordance with the operation speedof the robot 11.

The sensor 15 is attached to the end effector 17 of the robot 11. Indifferent embodiments, the sensor 15 may be attached to a supportstructure other than the robot 11. The sensor 15 comprises athree-dimensional sensor such as a stereo camera or a laser scanner, andgenerates machining results including three-dimensional data on asealing bead, a welding bead, or the like (hereinafter referred tosimply as “bead”). In an alternative embodiment in which the thicknessof the bead is constant, the sensor 15 may comprise a two-dimensionalsensor such as a CCD camera or a CMOS camera. In such a case, the sensor15 generates machining results including two-dimensional data on thebead. The sensor 15 transmits the machining results to the controller 14during machining or after machining.

FIG. 3 is a block diagram of the robot system 10 according to thepresent embodiment. The controller 14 comprises a control commandgeneration unit 21 for generating, by an operation program 20, controlcommands including the motion command issued to the robot 11, themachining command (or the flow rate command in the case of sealing)issued to the machining device 13, and the like. The control commandgeneration unit 21 is configured so as to perform position feedbackcontrol, speed feedback control, etc., so as to match the actualposition and the actual speed of the robot mechanism unit with a targetposition and target speed.

However, even if such control is performed, the robot 11 or theworkpiece 12 may vibrate due to a lack of rigidity of the robot 11, thejig 16, etc., whereby a machining path error relative to a target pathis generated. In addition to the machining path error, bead width errorand bead thickness error relative to the target width and targetthickness of the bead are generated due to the performance of themachining device 13. Thus, the controller 14 comprises a learningcontrol unit 22 for performing learning control based on machiningresults which take the entirety of the machining system intoconsideration.

The learning control unit 22 comprises a machining device performancecalculation section 24 for calculating, during machining or aftermachining, performance of the machining device 13 based on the motioncommand, machining command, and machining results stored in a firstmemory 23. The machining device performance calculation section 24calculates the actual bead amount per unit time from the motion commandand the machining results, and calculates the response performance ofthe machining device 13, which is a response time taken from theissuance of the machining command to the actual performance ofmachining, based on a time different between a changing point of themachining command and the actual changing point of the bead amount.Further, the machining device performance calculation section 24calculates tracking performance of the machining device 13 in responseto the machining command by calculating the actual amount of change perunit time of the bead amount. In other words, the performance of themachining device includes the response performance of the machiningdevice 13, which is a response time taken from the issuance of themachining command to the performance of machining, and trackingperformance of the machining device 13 in response to the machiningcommand.

The learning control unit 22 comprises a machining path errorcalculation section 25 for calculating the machining path error relativeto the target path based on the motion command and the machining resultsstored in the first memory 23. The machining path error calculationsection 25 calculates the machining path by calculating the target pathfrom the motion command and the central axis line of the bead from themachining results, and calculates the machining path error by comparingthe target path and the machining path.

The learning control unit 22 further comprises an operation positioncorrection information calculation section 28 for calculating currentoperating position correction information so as to satisfy an acceptablecondition of machining error stored in a second memory 26 based on prioroperating position correction information stored in a third memory 27.The operating position correction information calculation section 28does not update the prior operating position correction information whenthe machining path error satisfies the acceptable condition. Theoperating position correction information calculation section 28calculates the current operating position correction information so asto eliminate the machining path error when the machining path error doesnot satisfy the acceptable condition. The operating position correctioninformation calculation section 28 outputs the calculated currentoperating position correction information to the third memory 27 and alearning completion determination section 31, which is described later.The acceptable condition of the machining error stored in the secondmemory 26 may include at least one of a machining path tolerance, a beadwidth tolerance, and a bead thickness tolerance.

The learning control unit 22 further comprises an operation speedcorrection information calculation section 29 for calculating currentoperating speed correction information based on the machining path errorand prior operating speed correction information stored in the thirdmemory 27 so as to satisfy the acceptable condition of the machiningerror stored in the second memory 26 and under an allowable loadcondition of the robot mechanism unit stored in the second memory 26.The operation speed correction information calculation section 29increases the operation speed under the allowable load condition of therobot mechanism unit when the machining path error satisfies theacceptable condition, calculates a speed fluctuation allowable rangefrom the acceptable condition of the machining error when the machiningpath error does not satisfy the acceptable condition, and calculates thecurrent operation speed correction information so as to reduce theoperation speed under the allowable load condition of the robotmechanism unit while suppressing fluctuations in the operation speedwithin the speed fluctuation allowable range.

Further, the operation speed correction information calculation section29 calculates the current operation speed correction information basedon the tracking performance of the machining device 13 and the prioroperation speed correction information stored in the third memory 27 soas to satisfy the acceptable condition of the machining error stored inthe second memory 26 and under the allowable load condition of the robotmechanism unit stored in the second memory 26. The operation speedcorrection information section 29 calculates the current operation speedcorrection information so as to match the amount of change per unit timeof the motion command with the tracking performance of the machiningdevice 13 (i.e., the actual amount of change per unit time of the beadamount). The operation speed correction information calculation section29 outputs the calculated current operation speed correction informationto the third memory 27 and the learning completion determination section31, which is described later.

The learning control unit 22 further comprises a machining commandcorrection information calculation section 30 for calculating currentmachining command correction information based on the responseperformance and tracking performance of the machining device 13 and theprior machining command correction information stored in the thirdmemory 27 so as to satisfy the acceptable condition of the machiningerror stored in the second memory 26. The machining command correctioninformation calculation section 30 corrects the timing at which themachining command is performed based on the response performance of themachining device 13 and calculates the current machining commandcorrection information so as to match the amount of change per unit timeof the machining command with the tracking performance of the machiningdevice 13 (i.e., the actual amount of change per unit time of the beadamount). The machining command correction information calculationsection 30 outputs the calculated current machining command correctioninformation to the third memory 27 and the learning completiondetermination section 31, which is described later.

Additionally, the learning control unit 22 comprises a learningcompletion determination section 31 for determining whether or notlearning has completed by comparing the prior correction informationstored in the third memory 27 with the input current correctioninformation. The learning completion determination section 31 determinesthat learning has completed when the ratio of the values of the priorand current correction information is within a predetermined range, andconversely, determines that learning has not completed when the ratio ofthe values of the prior and current correction information is not withinthe predetermined range. When learning has completed, the learningcompletion determination section 31 outputs the converging correctioninformation to a fourth memory 32. The control command generation unit21 generates the motion command and machining command after learningbased on the converging correction information stored in the thirdmemory 27. The robot 11 and the machining device 13 perform machining ofthe workpiece 12 in accordance with the motion command and machiningcommand after learning.

The first memory 23, second memory 26, and third memory 27 are volatilememories, such as DRAM, for performing fast learning. Conversely, thefourth memory 32 is a nonvolatile memory, such as EEPROM, so that theconverging correction information can be reused even after electricalpower has been turned off. Furthermore, the robot system 10 may furthercomprise an acceptable condition setting means (not shown) with which ateacher can set, in advance, the acceptable condition of the machiningerror stored in the second memory 26. The acceptable condition settingmeans is composed of acceptable condition setting software, a monitorfor displaying a setting screen, and a keyboard and mouse for inputtingthe acceptable condition.

FIG. 4 is a flowchart showing the control method of the robot system 10according to the present embodiment. Such flowchart is executed by thelearning control unit 22 shown in FIG. 3. In step S10, the acceptablecondition of the machining error is set. In step S11, the learningoperation is executed and the machining results are measured. In stepS12, the machining path error and the machining device performance(i.e., response performance and tracking performance) are calculated.

In step S13, the current operating position correction information iscalculated, based on the machining path error, so as to satisfy thepreset acceptable condition of the machining error. Furthermore, thecurrent operation speed correction information is calculated, based onthe machining path error and the tracking performance of the machiningdevice, so as to satisfy the preset acceptable condition of themachining error and under an allowable load condition of the robotmechanism unit. Further, the current machining command correctioninformation is calculated based on the response performance and trackingperformance of the machining device, so as to satisfy the presetacceptable condition of the machining error.

In step S14, it is determined whether or not learning has completed bydetermining whether or not the ratio of the values of the prior and thecurrent correction information is within a predetermined range. Whenlearning has not been completed (NO in step S14), the operation returnsto step S11, and learning is repeated. When learning has completed (YESin step S14), the process proceeds to step S15, and the learning results(i.e., the converging correction information) are stored in thenonvolatile memory.

FIG. 5A is a graph showing the motion command issued to the robot 11,the machining command issued to the machining device 13, and the actualbead amount prior to learning. Prior to learning, the waveform of themachining command is identical to the waveform of the motion command andis generated with a timing which is slightly hastened as compared to themotion command. In this example, the changing point of the actual beadamount due to the response performance of the machining device 13 isslightly later than the changing point of the machining command, and theamount of change per unit time of the bead amount due to the trackingperformance of the machining device 13 is more moderate than the amountof change per unit time of the machining command.

FIG. 5B is a graph showing the motion command issued to the robot 11,the machining command issued to the machining device 13, and the actualbead amount after learning. After learning, the amount of change perunit time of the motion command matches the tracking performance of themachining device 13 (i.e., the amount of change per unit time of theactual bead amount). Furthermore, the timing at which the machiningcommand is performed is corrected (in this example, the timing at whichthe machining command is performed is hastened only by the responsetime) based on the response performance of the machining device 13(i.e., the response time taken from the issuance of the machiningcommand until machining is actually performed) and the amount of changeper unit time of the machining command matches the tracking performanceof the machining device 13 (i.e., the amount of change per unit time ofthe actual bead amount). As a result, the change in the actual beadamount and the change in the motion command match, and the bead amountis constant even at the corners, at which the operation speed changes.

FIG. 6A is a plan view showing the actual bead B1 at the corner prior tolearning. As described for FIG. 5A, though, prior to learning, as theoperation speed of the robot 11 decreases at the corners, the machiningcommand also decreases, the width and thickness of the bead B1 are notconstant due to the response performance and tracking performance of themachining device 13. Note that, each wavy line in FIG. 6 represents themoving distance of the robot per unit time and the actual bead amount iscalculated for each unit time.

FIG. 6B is a plan view showing the actual bead B2 at the corner afterlearning. After learning, even if the operation speed of the robot 11decreases at the corner, the amount of change per unit time of themotion command matches the tracking performance of the machining device13, and since the timing at which the machining command is performed iscorrected based on the response performance of the machining device 13,and the amount of change per unit time of the machining command matchesthe tracking performance of the machining device 13, the bead amountacross the entire corner is constant. Note that the present inventioncan be applied to other embodiments, in which the performance of themachining device 13 exceeds the performance of the robot 11.

FIG. 7 is a block diagram of a robot system 40 according to anotherembodiment, comprising a plurality of sets of machining systems 41 to43, and a server device 44 which is communicable with the plurality ofsets of machining systems 41 to 43. The plurality of sets of machiningsystems 41 to 43 each comprises the robot 11, machining device 13,controller 14, and sensor 15 mentioned above. The server device 44comprises a communication control unit 45 for controlling communicationwith the plurality of sets of machining systems 41 to 43, a learningcontrol unit 46 for performing learning control based on the motioncommands, machining commands, and machining results received from theplurality of sets of machining systems 41 to 43, and a storage unit 47for storing learning conditions and learning results. The learningcontrol unit 46 comprises the same constituent elements as the learningcontrol unit 22 of FIG. 3.

When the plurality of sets of machining systems 41 to 43 perform thesame machining on workpieces 12, the plurality of sets of machiningsystems 41 to 43 share at least one of the learning conditions andlearning results stored in the server device 44. Note that the learningconditions include the acceptable condition of the machining error andthe allowable load of the robot mechanism unit, and the learning resultsinclude the converging correction information. According to such aconfiguration, since the plurality of sets of machining systems 41 to 43can use at least one of the learning conditions and learning results ofthe other machining systems, the workload is reduced or the learningtime is reduced.

According to the present embodiment, learning can be controlled based onmachining results taking the entire system into consideration. As aresult, trial and error and skill of the teacher are not necessary, andthe workload can be reduced.

The invention claimed is:
 1. A robot system, comprising: a robot, amachining device for machining a workpiece, a controller for controllingthe robot and the machining device, a sensor for detecting machiningresults, and a learning control unit for performing learning controlbased on the machining results, wherein the learning control unitcomprises: a machining device performance calculation section forcalculating performance of the machining device during machining orafter machining based on a motion command issued to the robot by thecontroller, a machining command issued to the machining device by thecontroller, and machining results measured by the sensor, and anoperation speed correction information calculation section forcalculating operation speed correction information of the robot based onthe performance of the machining device so as to satisfy a presetacceptable condition of the machining error, and under an allowable loadcondition of the robot.
 2. The robot system according to claim 1,wherein the learning control unit further comprises a machining commandcorrection information calculation section for calculating machiningcommand correction information based on the performance of the machiningdevice so as to satisfy the preset acceptable condition of the machiningerror.
 3. The robot system according to claim 2, wherein the performanceof the machining device includes response performance of the machiningdevice, which is a response time taken from the issuance of themachining command to the actual performance of machining, and trackingperformance of the machining device in response to the machiningcommand.
 4. The robot system according to claim 3, wherein the machiningcommand correction information calculation section calculates themachining command correction information so as to correct timing of themachining command based on the response performance of the machiningdevice and to match the amount of change per unit time of the machiningcommand with the tracking performance of the machining device.
 5. Therobot system according to claim 3, wherein the operation speedcorrection information calculation section calculates operation speedcorrection information of the robot so as to match the amount of changeper unit time of the motion command with the tracking performance of themachining device.
 6. The robot system according to claim 1, wherein themachining device comprises a sealing device for performing sealing ofthe workpiece based on a sealant flow rate command which is themachining command, or an arc welding device for performing arc weldingon the workpiece based on a welding wire feed command which is themachining command.
 7. The robot system according to claim 1, wherein theacceptable condition of the machining error includes at least one of amachining path tolerance, a bead width tolerance, and a bead thicknesstolerance.
 8. The robot system according to claim 1, further comprisingan acceptable condition setting means with which a teacher can set, inadvance, the acceptable condition of the machining error.
 9. The robotsystem according to claim 3, wherein the machining device performancecalculation section calculates tracking performance of the machiningdevice by calculating an actual bead amount per unit time from themotion command and the machining results, calculating the responseperformance of the machining device based on a time difference between achanging point of the machining command and a changing point of theactual bead amount, and calculating an amount of change per unit time ofthe actual bead amount.
 10. The robot system according to claim 1,wherein the learning control unit further comprises a machining patherror calculation section for calculating a machining path errorrelative to a target path based on the motion command and the machiningresults, and the operation speed correction information calculationsection further calculates operation speed correction information of therobot based on the machining path error so as to satisfy the presetacceptable condition of the machining error, and under the allowableload condition of the robot.
 11. The robot system according to claim 10,wherein the learning control unit further comprises an operatingposition correction information calculation section for calculatingoperating position correction information of the robot based on themachining path error so as to satisfy the preset acceptable condition ofthe machining error.
 12. A robot system, comprising: a plurality of setsof machining systems each comprising a robot, a machining deviceconfigured to machine a workpiece, a controller configured to controlthe robot and the machining device, and a sensor configured to detectmachining results; a learning control unit configured to performlearning control based on the machining results, wherein the learningcontrol unit comprises: a machining device performance calculationsection configured to calculate, for at least one of the plurality ofsets of machining systems, performance of the machining device duringmachining or after machining based on a motion command issued to therobot by the controller, a machining command issued to the machiningdevice by the controller, and the machining results detected by thesensor, and an operation speed correction information calculationsection configured to, based on the performance of the machining device,calculate operation speed correction information of the robot so as tosatisfy a preset acceptable condition of a machining error, and under anallowable load condition of the robot; and a server device configured tocommunicate with the plurality of sets of machining systems, wherein theplurality of sets of machining systems share at least one of learningconditions and learning results stored in the server device.
 13. Therobot system according to claim 12, wherein the controller of one of theplurality of sets of machining systems comprises the learning controlunit.
 14. The robot system according to claim 12, wherein the serverdevice comprises the learning control unit.
 15. A method for the controlof a robot system comprising a robot, a machining device for machining aworkpiece, a controller for controlling the robot and the machiningdevice, a sensor for measuring machining results, and a learning controlunit for performing learning control based on the machining results, themethod comprising the steps of: calculating performance of the machiningdevice during machining or after machining based on a motion commandissued to the robot by the controller, a machining command issued to themachining device by the controller, and machining results measured bythe sensor, and calculating operation speed correction information ofthe robot based on the performance of the machining device so as tosatisfy a preset acceptable condition of the machining error, and underan allowable load condition of the robot.